jonstreeterandClaude
36860b09dd
This fixes the ModuleNotFoundError issues that occurred during clean installations. Changes: - Embedded colormnet_source dependencies directly into repository as: - colormnet_model/ (model architecture) - colormnet_inference/ (inference pipeline) - colormnet_util/ (utility functions) - colormnet_dataset/ (data transforms and utilities) - Updated all internal imports to use new embedded paths - Removed external colormnet_source path dependency from __init__.py - Added graceful fallback for spatial_correlation_sampler CUDA extension - Fixed shape calculation bugs in PyTorch fallback implementation - Added missing dependencies (hickle, scikit-image) to auto-install The repository is now self-contained and can be installed without requiring the separate colormnet_source directory. Tested and verified: - Node imports work correctly - Colorization pipeline runs successfully - Model loads and processes video frames Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
import numpy as np
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from PIL import Image
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import cv2
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import thinplate as tps
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cv2.setNumThreads(0)
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def pick_random_points(h, w, n_samples):
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y_idx = np.random.choice(np.arange(h), size=n_samples, replace=False)
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x_idx = np.random.choice(np.arange(w), size=n_samples, replace=False)
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return y_idx/h, x_idx/w
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def warp_dual_cv(img, mask, c_src, c_dst):
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dshape = img.shape
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theta = tps.tps_theta_from_points(c_src, c_dst, reduced=True)
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grid = tps.tps_grid(theta, c_dst, dshape)
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mapx, mapy = tps.tps_grid_to_remap(grid, img.shape)
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return cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR), cv2.remap(mask, mapx, mapy, cv2.INTER_NEAREST)
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def random_tps_warp(img, mask, scale, n_ctrl_pts=12):
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"""
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Apply a random TPS warp of the input image and mask
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Uses randomness from numpy
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"""
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img = np.asarray(img)
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mask = np.asarray(mask)
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h, w = mask.shape
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points = pick_random_points(h, w, n_ctrl_pts)
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c_src = np.stack(points, 1)
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c_dst = c_src + np.random.normal(scale=scale, size=c_src.shape)
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warp_im, warp_gt = warp_dual_cv(img, mask, c_src, c_dst)
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return Image.fromarray(warp_im), Image.fromarray(warp_gt)
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